Review & PResentation
See Through Walls with Wi-Fi! Fadel Adib and Dina Katabi Massachusetts Institute of Technology SIGCOMM’13, August 12–16, 2013, Hong Kong, China
outline
outline Background ~ Motivation & Previous work ~ The Big Picture, Challenges & Application Research Solution & Design ~ Basic idea - WIVI ~ How to eliminate ~ How to track Demo & Gesture Interface Implementation & Experimental Results Concluding Remarks & Future works
Can youR CellPhone give you XRay vision?
PRevious woRk
• Designed for military [RCP10] – 2 GHz bandwidth – Large power source – Huge antenna array • Attempt at using Wi-Fi [CSW12] – Needs to install a receiver in the imaged room and connect it to a receiver outside the room via wire
8 feet
the Big PiCtuRe
x αx
h1
h2
Challenges
Wall refection is 10,000x stronger than reflections coming from behind the wall
Tracking people from their reflections
aPPliCations Police avoids ambush
Personal security
Firefighters check for humans
Gesture interface from behind a wall
outline Background ~ Motivation & Previous work ~ The Big Picture, Challenges & Application Research Solution & Design ~ Basic idea - WIVI ~ How to eliminate ~ How to track Demo & Gesture Interface Implementation & Experimental Results Concluding Remarks & Future works
wi-vi: wiFi vision -- small, low-PoweR, Cost • • • • • •
Eliminate the wall’s reflection Detect and track people from reflections Gesture-based interface Implemented on software radios Wi-Vi uses Wi-Fi signals to see through walls Identify the number of moving humans and their relative locations • Allows humans to communicate without a transmitting device • Through-wall gesture based interface
Flash eFFeCt
Reflections off the wall overwhelm the receiver’s analog to digital converter, preventing it from registering the minute variations due to reflections from objects behind the wall. This behavior is called the “Flash Effect"
two main Challenges
two main Challenges
how Can we eliminate the wall’s ReFleCtion?
BasiC idea:
Transmit two waves that cancel each other when they reflect off static objects but not moving objects
Wall is static
disappears
People tend to move
detectable
eliminating the wall’s ReFleCtion Receive Antenna:
x Îąx
Transmit Antennas
eliminating the wall’s ReFleCtion Receive Antenna:
x
y = h1 x + h2αx
h1
α = -h1 / h2
αx
h2
0
eliminating all statiC ReFleCtions
eliminating all statiC ReFleCtions x αx
h1
y = h1 x + h2αx h2
Static objects (wall, furniture, etc.) have constant channels
y = h1 x + h2(- h1/ h2)x
0
People move, therefore their channels change y = h1’ x + h2’(- h1/ h2)x
Not Zero
how Can we tRaCk using ReFleCtions?
tRaCking motion
RF source
θ
Direction of reflection
Antenna Array
tRaCking motion Direction of motion
θ At any point in time, we have a single measurement
Antenna Array
tRaCking motion Direction of motion
θ θ Direction of motion Antenna Array
tRaCking motion Direction of motion
θ θ Direction of motion
Human motion emulates antenna array Antenna Array
outline Background ~ Motivation & Previous work ~ The Big Picture, Challenges & Application Research Solution & Design ~ Basic idea - WIVI ~ How to eliminate ~ How to track Demo & Gesture Interface Implementation & Experimental Results Concluding Remarks & Future works
a thRough-wall gestuRe inteRFaCe
• Sending Commands with Gestures • Two simple gestures to represent bit ‘0’ and bit ‘1’ • Can combine sequence of gestures to convey longer message
gestuRe enCoding
Bit ‘0’: step forward followed by step backward Bit ‘1’: step backward followed by step forward Step Forward
Step Backward
gestuRe deCoding Matched Filter
0
2
4
6 8 T im e (S e c o n d s)
10
12
14
Peak Detector Bit ‘0’
Bit ‘1’
Gesture interface that works through walls and none-line-of-sight 0
2
4
6 8 Time (Seconds)
10
12
1
outline Background ~ Motivation & Previous work ~ The Big Picture, Challenges & Application Research Solution & Design ~ Basic idea - WIVI ~ How to eliminate ~ How to track Demo & Gesture Interface Implementation & Experimental Results Concluding Remarks & Future works
imPlementation and evaluation
• USRP software radios: – Wi-Fi signals: 2.4GHz, OFDM – 5MHz to enable real-time channel estimation – 10mW transmit power
• Old and new buildings on MIT campus • Eight human subjects – 200 experiments
tRaCking multiPle humans
One moving person is indicated by a single curvy line
tRaCking multiPle humans
• Number of distinct curves at the same time corresponds to the number of humans Two Humans
100
60
60
40
40
20 0 -20 -40 -60
2
20 0 -20 -40 -60
-80 -100
1
80
Angle (degrees)
Angle (degrees)
100
2
80
Three Humans
1 0
1
2
3
-80 3 4 Time (seconds)
5
6
7 -100 0
1
2
3 4 Time (seconds)
5
6
7
automatiC estimation: numBeR oF PeoPle
Detected
0
1
2
3
0
100%
0%
0%
0%
1
0%
100%
0%
0%
2
0%
0%
85%
15%
3
0%
0%
10%
90%
Actual
Building mateRials 100
100
100
100
100 87.5
Accuracy (%)
80 60 40 20 0
Free Space
1.75 wood door
8 concrete
gestuRes: aCCuRaCy vs. distanCe Bit '0'
Bit '1'
Accuracy (%)
100 80 60 40 20 0
1
2
3
4
5
6
7
Distance from the wall (meters)
8
9
ConClusion Wi-Vi bridges advance networking techniques with human-computer interaction. It motivates a new form of user interfaces which rely solely on using the reflections of a transmitted RF signal to identify human gestures. More improvements will further allow Wi-Vi to capture higher quality images enabling the gesturebased interface to become more expressive hence promising new directions for virtual reality
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